class_docstr: TorchRankerAgent implementation of the biencoder.
  
  It is a standalone Agent. It might be called by the Both Encoder.
code: def __init__(self, opt, shared=None):
      # download pretrained models
      download(opt["datapath"])
      self.pretrained_path = os.path.join(
          opt["datapath"], "models", "bert_models", MODEL_PATH
      )
      opt["pretrained_path"] = self.pretrained_path
  
      self.clip = -1
  
      super().__init__(opt, shared)
      # it's easier for now to use DataParallel when
      self.NULL_IDX = self.dict.pad_idx
      self.START_IDX = self.dict.start_idx
      self.END_IDX = self.dict.end_idx
      # default one does not average
      self.rank_loss = torch.nn.CrossEntropyLoss(
          reduce=True, size_average=True
      )
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.utils.torch import padded_3d
  from parlai.zoo.bert.build import download
  from parlai.utils.io import PathManager
  from .bert_dictionary import BertDictionaryAgent
  from .helpers import BertWrapper, BertModel, add_common_args, surround, MODEL_PATH
  import os
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 2 to create a new BiEncoderRankerAgent object from inherited base classes: TorchRankerAgent with example: obj = BiEncoderRankerAgent(opt, shared)